Segmentation of the Proximal Femur by the Analysis of X-ray Imaging Using Statistical Models of Shape and Appearance

被引：1

作者：

Guillen, Joel Oswaldo Gallegos ^{[1
]}

Cerquin, Laura Jovani Estacio ^{[1
]}

Obando, Javier Delgado ^{[2
]}

Castro-Gutierrez, Eveling ^{[1
]}

机构：

[1] San Agustin Natl Univ Arequipa, Arequipa, Peru

[2] Univ Austral Chile, Valdivia, Chile

来源：

ARTIFICIAL INTELLIGENCE AND SOFT COMPUTING (ICAISC 2018), PT II | 2018年 / 10842卷

关键词：

Segmentation; AP X-ray; Statistical shape models (SSM); Statistical appearance models (SAM); Gold standard; DICE coefficient; REGISTRATION;

D O I：

10.1007/978-3-319-91262-2_3

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Using image processing to assist in the diagnostic of diseases is a growing challenge. Segmentation is one of the relevant stages in image processing. We present a strategy of complete segmentation of the proximal femur (right and left) in anterior-posterior pelvic radiographs using statistical models of shape and appearance for assistance in the diagnostics of diseases associated with femurs. Quantitative results are provided using the DICE coefficient and the processing time, on a set of clinical data that indicate the validity of our proposal.

引用

页码：25 / 35

页数：11

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